SparkSession and SparkContext in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a SparkSession named spark.

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName([1]).getOrCreate()

Drag options to blanks, or click blank then click option'

A"MyApp"

BMyApp

Cspark

DSparkSession

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to put the app name in quotes.

Using a variable name without quotes.

The appName method requires a string argument with the application name, so it must be in quotes.

2fill in blank

medium

Complete the code to get the SparkContext from the SparkSession.

Apache Spark

sc = spark.[1]

Drag options to blanks, or click blank then click option'

Acontext

BsparkContext

CSparkContext

DgetContext

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'SparkContext' instead of 'sparkContext'.

Trying to call a method instead of accessing an attribute.

The SparkContext is accessed from SparkSession by the attribute sparkContext (camelCase).

3fill in blank

hard

Fix the error in the code to stop the SparkSession properly.

Apache Spark

spark.[1]()

Drag options to blanks, or click blank then click option'

Astop

BstopSession

Cclose

Dend

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'close()' or 'end()' which do not exist.

Trying to call 'stopSession()' which is incorrect.

The correct method to stop a SparkSession is 'stop()'.

4fill in blank

hard

Fill both blanks to create a SparkSession with master set to local and app name 'TestApp'.

Apache Spark

spark = SparkSession.builder.master([1]).appName([2]).getOrCreate()

Drag options to blanks, or click blank then click option'

A"local"

B"TestApp"

Clocal

DTestApp

Attempts:

3 left

💡 Hint

Common Mistakes

Not using quotes around 'local' or 'TestApp'.

Using unquoted values causing syntax errors.

Both master and appName require string arguments in quotes.

5fill in blank

hard

Fill all three blanks to create a dictionary with word lengths for words longer than 3 characters.

Apache Spark

lengths = { [1]: [2] for [3] in words if len([3]) > 3 }

Drag options to blanks, or click blank then click option'

Aword

Blen(word)

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names for key and loop variable.

Not using len(word) for the value.

The dictionary comprehension uses 'word' as key and 'len(word)' as value, iterating over 'word' in words.

Practice

1. What is the primary role of SparkSession in Apache Spark?

easy

A. It stores data permanently in Spark.

B. It manages the cluster resources directly.

C. It is the main entry point to work with Spark SQL and DataFrames.

D. It replaces the need for any SparkContext.

Solution

1. Step 1: Understand SparkSession's purpose

   SparkSession is designed as the main interface for working with structured data using Spark SQL and DataFrames.

2. Step 2: Differentiate from other components

   SparkContext manages cluster connection but SparkSession provides higher-level APIs for SQL and DataFrames.

3. Final Answer:

   It is the main entry point to work with Spark SQL and DataFrames. -> Option C

4. Quick Check:

   SparkSession = main SQL/DataFrame entry [OK]

Hint: SparkSession is your main tool for SQL and DataFrames [OK]

Common Mistakes:

• Confusing SparkSession with SparkContext's cluster management
• Thinking SparkSession stores data permanently
• Believing SparkSession removes need for SparkContext

2. Which of the following is the correct way to create a SparkSession in PySpark?

easy

A. spark = SparkContext.builder.appName('MyApp').getOrCreate()

B. spark = SparkSession.builder.appName('MyApp').getOrCreate()

C. spark = SparkSession.create('MyApp')

D. spark = SparkContext.create('MyApp')

Solution

1. Step 1: Recall SparkSession creation syntax

   The correct way is to use SparkSession.builder, set app name, then call getOrCreate() to create or get existing session.

2. Step 2: Identify incorrect options

   SparkContext does not have builder or getOrCreate methods; SparkSession.create is not valid syntax.

3. Final Answer:

   spark = SparkSession.builder.appName('MyApp').getOrCreate() -> Option B

4. Quick Check:

   Use SparkSession.builder.appName().getOrCreate() [OK]

Hint: Use SparkSession.builder.appName().getOrCreate() to start [OK]

Common Mistakes:

• Using SparkContext instead of SparkSession to create session
• Calling non-existent create() method
• Missing getOrCreate() at the end

3. Given the following PySpark code, what will be the output of print(sc.appName)?

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('TestApp').master('local').getOrCreate()
sc = spark.sparkContext
print(sc.appName)

medium

A. None

B. local

C. SparkContext

D. TestApp

Solution

1. Step 1: Understand appName assignment

   The appName 'TestApp' is set when building SparkSession and is passed to SparkContext.

2. Step 2: Access SparkContext appName property

   sc.appName returns the application name set, which is 'TestApp'.

3. Final Answer:

   TestApp -> Option D

4. Quick Check:

   sc.appName = 'TestApp' [OK]

Hint: sc.appName matches SparkSession appName [OK]

Common Mistakes:

• Confusing master URL with appName
• Expecting sc.appName to be 'SparkContext'
• Assuming sc.appName is None by default

4. Identify the error in the following PySpark code snippet:

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('App').getOrCreate()
sc = spark.sparkContext
sc = SparkContext('local', 'App')
print(sc.appName)

medium

A. Reassigning sc with SparkContext directly causes error.

B. Missing import for SparkContext.

C. SparkSession.builder.appName() is incorrect syntax.

D. print(sc.appName) is invalid method call.

Solution

1. Step 1: Analyze sc reassignment

   After creating SparkSession and getting sc, the code tries to create a new SparkContext directly, which is not allowed if one already exists.

2. Step 2: Understand SparkContext singleton rule

   Only one SparkContext can exist per JVM; creating another causes an error.

3. Final Answer:

   Reassigning sc with SparkContext directly causes error. -> Option A

4. Quick Check:

   Only one SparkContext allowed; reassigning causes error [OK]

Hint: Do not create SparkContext if SparkSession exists [OK]

Common Mistakes:

• Trying to create multiple SparkContext instances
• Ignoring SparkContext singleton restriction
• Assuming print(sc.appName) is invalid

5. You want to create a SparkSession with the app name 'DataApp' and run it locally using 4 cores. Which code snippet correctly achieves this and also retrieves the SparkContext?

hard

A. spark = SparkSession.builder.appName('DataApp').master('local[4]').getOrCreate()
sc = spark.sparkContext

B. sc = SparkContext('local[4]', 'DataApp')
spark = SparkSession(sc)

C. spark = SparkSession.create('DataApp', master='local[4]')
sc = spark.getContext()

D. spark = SparkSession.builder.master('4').appName('DataApp').getOrCreate()
sc = spark.context

Solution

1. Step 1: Use SparkSession.builder with appName and master

   The correct way is to use builder, set appName to 'DataApp' and master to 'local[4]' for 4 cores, then call getOrCreate().

2. Step 2: Retrieve SparkContext from SparkSession

   Access SparkContext via spark.sparkContext property after creating SparkSession.

3. Final Answer:

   spark = SparkSession.builder.appName('DataApp').master('local[4]').getOrCreate() sc = spark.sparkContext -> Option A

4. Quick Check:

   Use builder.appName().master('local[4]').getOrCreate() + spark.sparkContext [OK]

Hint: Set master='local[4]' and get spark.sparkContext [OK]

Common Mistakes:

• Using SparkContext constructor directly instead of SparkSession.builder
• Wrong master string like '4' instead of 'local[4]'
• Incorrect method names like create() or getContext()